ReviewInnovation (Cambridge (Mass.))2026
Fusion sensing for urban resilience.
Review in Innovation (Cambridge (Mass.)), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Urban resilience is crucial for achieving a city's sustainable development goals in the context of global change. However, a lack of sensing and intelligent analytical frameworks that can monitor the fine-grained spatiotemporal dynamics of the stressors and a city's reactions to these stressors is largely prohibiting the smart decision support for urban resilience studies. Here, we propose the integration of multi-source sensing technologies, including remote sensing, social sensing, and IoT-based sensing, collectively referred to as "fusion sensing," to enhance our monitoring capability of urban systems. These technologies enable real-time data collection and analysis, facilitating adaptive responses to disturbances. Additionally, the artificial intelligence methods play important roles in both short-term emergency management and long-term planning, optimizing resource allocation and resilience. Hence, we call for collaboration among governments, industry, academia, and individuals to leverage artificial intelligence and various sensing technologies, thereby enhancing urban resilience through proactive and informed decision-making.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.